Asian Journal of Research in Social Sciences and Humanities
Year : 2016, Volume : 6, Issue : 10
First page : ( 1998) Last page : ( 2009)
Online ISSN : 2249-7315.
Article DOI : 10.5958/2249-7315.2016.01148.5

An Efficient Hybrid Neural Network Model for Wind Speed Prediction

Ranganayaki V.*, Deepa S. N.**

*Dr. NGP Institute of Technology, Coimbatore, India

**Anna University Regional Centre, Coimbatore, India

Online published on 14 October, 2016.

Abstract

This paper deals with renewable energy system needs a hybrid neural network model on predicting the accurate wind speed. The wind speed and wind direction is fluctuating in nature. Therefore short term and long term wind speed forecast is more significant for establishing the availability of wind power generation. The objective is to compute the wind speed using hybrid neural network models which includes Self-Organizing Map and Radial Basis Function neural network. The proposed SOM-RBF model uses three input data such as wind speed (m/s), wind direction (degree) and temperature (degree Celsius) collected from Coimbatore wind farm. In this work, the SOM-RBF hybrid model used to predict a day ahead wind speed using past data. The experimental results show that root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) is much lesser and correlation coefficient is reaches to one. This validates the suitability of SOM-RBF model for wind speed prediction task than single models.

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Keywords

Hybrid Model, Radial Basis Function, Wind Speed Prediction, SOM.

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